Base station identification based location quality data determination

ABSTRACT

Determining a location quality based on base station identification is disclosed. The location quality can be based on an error attributed to a location determined based on historical data related to an identified base station. Application of supplemental data to the historical base station data can improve location quality by reducing the error. Supplemental data can comprise Voronoi data, geographic data, historical UE density data, historical UE timing advance data, or combinations thereof. Voronoi data can be associated with an area less than a service area of the base station. Geographic data can indicate areas where UEs are not likely to be located. UE density data can indicate probably UE locations. Timing advance data can indicate annular regions where a UE should be located. As such, the supplemental data can constrain a location determined for a UE and correspondingly can reduce error associated with the location.

RELATED APPLICATION

The subject application is a continuation of, and claims priority to,U.S. patent application Ser. No. 15/923,809, now issued as U.S. Pat. No.10,206,195, filed 16 Mar. 2018, and entitled “BASE STATIONIDENTIFICATION BASED LOCATION QUALITY DATA DETERMINATION,” which is acontinuation of, and claims priority to, U.S. patent application Ser.No. 15/372,316, now issued as U.S. Pat. No. 9,949,230, filed 6 Dec.2017, and entitled “BASE STATION IDENTIFICATION BASED LOCATION QUALITYDATA DETERMINATION,” the entireties of which applications are herebyincorporated by reference herein.

TECHNICAL FIELD

The disclosed subject matter relates to determining location qualitydata for location information based on an identification of a basestation or an identification of a sector of a base station.

BACKGROUND

Location data quality can be associated with an amount of errorattributed to a determined location. Generally, the lower the errorassociated with the location data, the higher the location data qualityis considered. Higher quality location data can generally be consideredmore valuable by prospective consumers of location data. In an aspect,lower error for determined location data can allow the location data tobe employed in technologies that can rely on a certain level of locationdata accuracy. While some location determination technologies can havebetter location data quality than other location determinationtechnologies, often the more accurate location determinationtechnologies, such as global positioning system (GPS), etc., can beconsidered power hungry technology, e.g., they can have a notable impacton operation times for battery-powered devices, such as smartphones,tablet computers, laptop computers, etc. In contrast, often lessaccurate, e.g., lower quality, conventionally determined location data,can be associated with more battery friendly location determinationtechnology.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is an illustration of an example system that can enabledetermining location quality data based on identification of a basestation device in accordance with aspects of the subject disclosure.

FIG. 2 is an illustration of examples of supplementary cell data thatcan be employed in determining location quality data based onidentification of a base station device in accordance with aspects ofthe subject disclosure.

FIG. 3 is an illustration of an example of combining Voronoi-typesupplementary cell data and geographic-type supplementary cell data indetermining location quality data based on identification of a basestation device in accordance with aspects of the subject disclosure.

FIG. 4 is an illustration of an example of combining Voronoi-typesupplementary cell data, geographic-type supplementary cell data, andhistorical UE density-type supplementary cell data in determininglocation quality data based on identification of a base station devicein accordance with aspects of the subject disclosure.

FIG. 5 illustrates an example of employing historical UE density-typesupplementary cell data, with or without geographic-type supplementarycell data, in determining location quality data based on identificationof a base station device in accordance with aspects of the subjectdisclosure.

FIG. 6 illustrates an example of combining historical UE timingadvance-type supplemental cell data and historical UE density-typesupplementary cell data in determining location quality data based onidentification of a base station device in accordance with aspects ofthe subject disclosure.

FIG. 7 illustrates an example method facilitating determining locationquality data based on identification of a base station device inaccordance with aspects of the subject disclosure.

FIG. 8 illustrates an example method enabling determining locationquality data based on identification of a base station device inaccordance with aspects of the subject disclosure.

FIG. 9 illustrates an example method enabling a UE to receive locationquality data, wherein the location quality data is based onidentification of a base station device, in accordance with aspects ofthe subject disclosure.

FIG. 10 depicts an example schematic block diagram of a computingenvironment with which the disclosed subject matter can interact.

FIG. 11 illustrates an example block diagram of a computing systemoperable to execute the disclosed systems and methods in accordance withan embodiment.

DETAILED DESCRIPTION

The subject disclosure is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the subject disclosure. It may be evident, however,that the subject disclosure may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to facilitate describing the subjectdisclosure.

Some location technologies can provide location information withlocation quality data for a user equipment (UE). Location quality datacan comprise an amount of error determined to be associated with theprovided location information. As an example, global positioning system(GPS) location information can be subject to an amount of errorcorrelated to a number of visible satellites in a satelliteconstellation employed by a device determining a GPS location and, assuch, based on the number of satellites in the constellation, locationquality data, e.g., error associated with correspondingly determined GPSlocation information, can be provided based on the number of satellites.Moreover, modern UEs can be connected to a wireless network, e.g., via abase station (BS), and the locations of the UEs can generally beassociated with the location of the BS. However, BS location informationcan be employed as a UE location without location quality data or, onlymarginally better, BS location information can be used as a UE locationwith the error given as the diameter of the coverage area for anomnidirectional BS or a length of a lobe for a BS having one or moresectors. Where a BS has a small coverage area, e.g., in parts of SanFrancisco some BS coverage areas can be about 500 meters in diameter,the location quality data can indicate a higher level of accuracy that,for example, a BS with a large coverage area, e.g., in rural Montanawhere a BS coverage area can be several kilometers in diameter. It isoften desirable that location quality data be available such that anaccuracy of the location information can be considered when the locationinformation is consumed. The more accurate the location, often, the morevalued the location information can be considered. As such, it can bedesirable to improve determinations of location quality data, e.g., themore accurately error in the location information can be determined, thebetter the location quality data can be in representing an accuracy ofthe location information itself.

Some location technologies generally provide location information foruser equipment (UE) in wireless network service areas. The locationinformation can be, where available, associated with location qualitydata (LQD) as a metric allowing a consumer of the location data to gaugehow useful the location data is for a particular purpose. Locationquality data can comprise an indicated amount of error determined to beassociated with the provided location information. As an example,location data can be employed in conjunction with point of sale (POS)technology, whereby a location of a UE can be checked against a POS toaid in verifying that the transaction is legitimate, e.g., where a UEassociated with a user's credit card is located proximate to a POS wherethe credit card is being used, it can be more likely to be a validtransaction while, in contrast, if the UE is located far from the POS,the transaction can be questioned as invalid. As another example, anamount of error, e.g., the quality of the location data, can beconsidered important in an emergency call, e.g., 911 call. Where thelocation quality data indicates that the UE is within several tens ofmeters of the reported UE location at the time of the 911 call, theresponder can have a higher confidence that they will be able to locatethe distressed party in comparison to the location quality dataindicating that the UE is within several kilometers of the reportedlocation of the UE.

Modern UEs can be connected to a wireless network, e.g., via a basestation (BS), and the locations of the UEs can generally be associatedwith the location of the BS. Further, BS location information can besubstituted for a UE location where other forms of determining UElocation information are either not available or are less favored, e.g.,high power consumption on battery powered devices, intermittentinterruptions of location determination process by the other techniques,etc. Generally, substitution of a BS location for the UE location inconventional systems is provided without location quality data, or evenwhere location quality data for the BS substitution is available,conventionally provided location quality data can often be only asaccurate as a diameter of a coverage area for an omnidirectional BS, ora metric of a sector/lobe for a BS having one or more sectors. Inconventional systems, a BS that has a small coverage area, for example300 meters, where location quality data is even available, can indicatea higher level of accuracy than a BS with a large coverage area, forexample 10 kilometers. Where it is typically desirable that locationquality data be available and that the location quality data be moreaccurate than just reporting a size of a coverage area for a particularBS, such that an accuracy of the location information is improved oversimply reporting that a UE is located somewhere in the coverage area ofa BS, the herein disclosed techniques can provide for determininglocation quality data based on supplemental call data for an identifiedBS. Further, where more accurate location information, e.g., lessreported error in the location, can often be more valuable locationinformation, the disclosed location quality data determinationtechnology can be associated with a higher profit margin where locationquality data is commoditized.

In an aspect, LQD can be associated with cell identification data(CellID). CellID can comprise identifying information for a BS. As usedherein, the term BS can refer to a cellular base station, e.g., NodeB,eNodeB, etc., to an access point (AP), e.g., femtocell, picocell, Wi-FiAP, etc., or nearly any other device comprising a radio adapted forwireless communication with a UE. A location can be associated with theBS, e.g., the BS can be installed at a determined location, a locationcan be reported for a BS, a location can be determined for a BS via avariety of location determination techniques etc. Where a locationdetermined for the BS, said location can be substituted for a UE that iswithin the service area of the BS based on the premise that where the UEis close enough to ‘see’ the BS, the location of the UE is proximateenough to be considered the same as the BS location in the absence ofother more accurate UE location information. As an example, where a UEis coupled to a NodeB in an urban environment and the UE lacks othermore accurate location determination technology, the location of theNodeB can be used as the location of the UE. In this example, where theNodeB, for example, can have a service area of perhaps 1 kilometer indiameter, e.g., the extents of the service area can be 500 meters awayfrom the NodeB located in the center of the service area, and thelocation of the UE can be presumed to be located at the locationreported for the NodeB+/−500 meters. In contrast, for example in highlyrural areas, a BS can have a service area that can be much larger, suchas 10 km, whereby the location of the UE can be reported as the locationof the BS+/−5 km. However, supplemental cell data can be employed toimprove the reported LQD.

In an embodiment, Voronoi techniques can be used to determine a Voronoiboundary around a BS, e.g., based on the location of other BSs. TheVoronoi boundary can be different from the extents of the BS servicearea, wherein the Voronoi edge is a mathematical determination of allpoints closer to the BS than to any other BS. As an example, a Voronoiedge will typically bisect the straight-line distance between two BSsregardless of the coverage area of the BSs. However, where BSs aretypically located such that the corresponding service areas have someoverlap so as to avoid dead zones, e.g., areas of no service coverage,the Voronoi edge can actually be closer to both BSs than the extents ofa service area corresponding to each BS of the BSs. Further,statistically, a UE can be anywhere in the service area of the BS,however, where a Voronoi boundary is substituted for the extents of theBS, and where in well covered network service areas the Voronoiboundaries can correspond to smaller cells than those associated withthe extents of the coverage areas BSs comprising the network, thesubstituted Voronoi boundary can already provide an improved LQD wherethere can be a smaller Voronoi cell area than service area cell for thesame BS. Further, statistically, a UE can be assumed to be 50% likely tobe within an area comprising the closest 50% of the Voronoi cell to theBS and 50% likely to be within an area comprising the furthest 50% ofthe Voronoi cell from the BS, e.g., given a unit circle, 50% of the areaof the circle closest to the center of the circle is geometricallycircumscribed by a radius 0.707 units from the center of the circle,while the remaining 50% of the area is geometrically comprised in theband from 0.707 to 1.0 units from the center of the circle. Moreover,the distance from the BS location to the edge of the Voronoi cell can bedetermined, either continuously or at increments, such as at 0, 10, 20,30 . . . 350-degrees (every 10 degrees), etc., then averaged, which canprovide an average distance between the BS and the Voronoi boundary. Theaverage distance can then be multiplied by 0.707 to determine the 50%distance, e.g., the distance at which 50% of the area is inside thedistance and 50% is outside the distance. This 50% distance can then beused as LQD, indicating that a UE is50% likely to be inside the distanceand 50% likely to be outside the distance, see for example FIG. 3. Assuch, the BS location can be substituted for the UE location and a LQDof the 50% distance to the average Voronoi boundary can be reported,which can be an improvement over conventional techniques that report thesame location and either no LQD or an LQD that is frequently a fullradius of the extents of the coverage area.

In another aspect, geographic-type SCD can be employed to improve LQD.In view of UEs generally being excluded from certain areas within acoverage area of a BS or a Voronoi cell corresponding to the BS, theseexcluded areas can be removed from the possible locations of the UE toimprove the LQD. As an example, where 30% of a BS coverage areaencompasses a lake, cemetery, extremely steep terrain, garbage dump,etc., it can be assumed that the likelihood of a UE being located inthat 30% can be low, e.g., low enough that the area should be excludedfrom consideration for a likely location of a UE. As such, thegeographic-type SCD can be employed to map out exclusion zones where aUE will not be, e.g., even though a UE could be in the exclusion zone,there is such low probability of the UE actually being in an exclusionzone that the area of the exclusion zone is simply not considered ashaving a UE therein. Removal of an exclusion zone area fromconsideration can allow the coverage area and/or the Voronoi cell areathat supports UEs to be smaller, which is associated with reporting moreaccurate LQD. As an example, where a Voronoi cell for a BS includes alake, and the exclusion of the lake area reduces the area of the Voronoicell by 30%, the average Voronoi distance, e.g., where the Voronoi cellboundary is now further bounded by the edge of the lake, can be reduced,which in turn can reduce the 50% distance and the LQD. Of note, wherethe extents of the coverage area, e.g., rather than the Voronoi cell,can also be reduced, the LQD can similarly be better where the lake areais excluded as compared to the full extents of the coverage area.

In a further aspect, historical UE density-type SCD can be employed toimprove LQD. Historical UE density data can indicate a count of UEs in aparticular location, e.g., typically more granular locations than acoverage area of a BS. As an example, a geographic area can be overlaidwith a 100 meter by 100 meter grid pattern and the UE count within each100 m×100 m grid cell can be determined, e.g., typically by otherlocation determination technologies such as GPS, etc. Over time, thecount data of the example grid pattern can illustrate UE density for theregion in each grid cell. In some embodiments, the historical UE densitycan be represented by a heat-map image, see historical UE density-typeSCD 246, etc. The historical UE density can further be correlated to aserving BS based on which BS a UE reports when the location of the UE isreported for inclusion in the density data. As such, UE density for anarea associated with a particular BS can be determined and, where forexample, UEs are reported at the edge of the service area for the BS,the extents of a BS coverage area can be determined over time.Interestingly, excluded areas, such as the lake example above, can alsobecome evident in historical UE density data, namely, there can be anexceeding low count of UEs in excluded areas, or conversely, an excludedarea can be defined by a threshold count of UEs. As an example,historical UE density in Cleveland can have a substantial historical UEcount in areas of the city and dramatically lower counts of UEs in LakeErie. As such, a threshold value can be selected such that Lake Erie istreated as an exclusion zone. Accordingly, for a BS along the shores ofLake Erie in Cleveland, a portion of the coverage area can be excludedas a location for a UE. Similarly, a Voronoi cell can also exclude thelake area. Moreover, a boundary based on historical UE density for a BScan also exclude the lake. As such, a historical UE density basedboundary can be determined for a BS, such that the 50% of averagedistance between the BS and the boundary can be determined and used inreporting LQD. Similar logic applies to reporting LQD based on servicearea extents excluding the lake, or to a Voronoi cell boundary excludingthe lake.

Moreover, historical UE density-type SCD can enable determining of acentroid or a weighted centroid for an area including a service area ofa BS. As stated above, historical UE density data can generally indicatean area of service coverage for a BS, e.g., UEs typically only report aBS when they can see the BS at their reported location. As such, shapecan be determined from the historical UE density data that relates to ameasured service area of the BS. A geometric centroid can be determinedfrom the shape. This geometric centroid can be substituted for areported UE location rather than substituting a known location of theassociated BS, e.g., a UE is more likely to be in the center of theshape relating to the coverage area of the BS than at the location ofthe BS itself. Furthermore, the LQD can then be determined based on thedistance between the geometric centroid and the historical locations ofthe UEs from the historical UE density data, e.g., LQD can indicate thatthe error in the reported location of a UE is the average distancebetween the geometric centroid and the historically reported UElocations, which can automatically include exclusion zones via belowthreshold counts of UEs in areas of the historical UE density datacorresponding to an exclusion zone.

In some embodiments, a Voronoi cell can be used to select historical UEdensity data, e.g., only historical UE density data within a Voronoicell for a BS is considered, rather than determining a shape for basedon all reported historical UE density data for the BS. As such, thegeometric centroid can be determined for the Voronoi cell area via thehistorical UE density data. Alternatively, a centroid for the Voronoicell shape can be directly determined. However, in view of a weightedcentroid computation, use of historical UE density data to determine ashape of selecting a shape encompassed by a Voronoi cell for a BS canprovide a different centroid location. A weighted centroid can considernot only the shape, but also the count of UEs in portions of the shape.As such, situations where UEs are not evenly distributed across aservice area, the Voronoi cell of the corresponding BS, or representedin historical UE density data, the centroid can be shifted closer todenser populations and farther from less dense populations of UEs. Theweighted centroid can be understood to reflect a higher likelihood of aUE being located where there is a higher historical density of UEs thanwhere there is a lower historical density of UEs. As an example, wherehistorical UE density data indicates that a shopping mall has a higherUE density than a park across the street from the shopping mall, a queryas to a UE location can reflect that the UE is more likely to be at themall than in the park. Accordingly, a weighted centroid based on thehistorical UE density data can be determined. The LQD can then be basedon an average distance between each historical UE location and theweighted centroid. As before in the geometric centroid, a Voronoi cellcan be employed to select which UEs are to be considered in determiningthe weighted centroid and corresponding LQD. Similarly, a BS servicearea can be employed to select the UEs considered in in determining theweighted centroid and corresponding LQD. Moreover, a shape representingthe BS service area can be determined from the historical UE densitydata and this shape can be employed in selecting which UEs are used indetermining the weighted centroid and corresponding LQD.

In a further aspect, historical UE timing advance-type SCD can beemployed to improve LQD. Timing advance (TA) data is well understood toreflect a distance between a UE and a service BS and is employed incorrecting timing of radio interactions to account for the time that ittakes for radio waves to propagate over the distance between the UE andthe BS. TA data can be used to determine an annular area, or portionthereof corresponding to a sector of the BS, in which the UE is locatedto a serving BS. This annular region is typically one unit of TA inwidth, e.g., for global system for mobile communications (GSM) cellularservice, a unit of TA is about 550 meters deep or the distance traveledby a radio wave in about 3.69 microseconds. In an omnidirectional BS,this TA ring typically extends 360 degrees around the BS. In asectorized BS, for example where each of three sectors spans 120degrees, the TA ring can divided into three arcs of 120 degree width andone TA unit deep, which can be referred to herein as a TA banana, itbeing noted that a TA banana can have other widths and/or depths withoutdeparting from the scope of the present disclosure. In an aspect, wherea TA is known, the region of the UE is accordingly constrained. As such,historical UE TA-type SCD can enable determining a restricted areacorresponding to the historical TA. This can be employed several ways.In an aspect, the historical TA information can be used to determine arestricted area corresponding to a TA value for an identified BS, aloneor in combination with historical UE density data, geographic data,Voronoi data, etc. These determinations can be stored, such that inresponse to a query identifying a BS and a TA, a location in therestricted area can be associated with an error constrained by theextents of the restricted area, e.g., resulting from the TA banana,exclusion zones form geographic data or implied in historical UE densitydata, within the Voronoi cell, etc. As can be appreciated, the TA datacan serve to reduce possible locations for a UE and as such, the LQD canimprove.

In an aspect, any combination of Voronoi-type SCD, geographical-typeSCD, historical UE density-type SCD, historical UE timing advance-typeSCD, etc., can be employed to reduce areas where a UE is likely to belocated and, accordingly, a reported location for a UE can be reportedwith an improved LQD where the several types of SCD are employed toreduce the possible area in which the UE can be located, e.g., the errorof associated with a reported location for a UE is typically lower wherethe area that the UE can be in is smaller. It can be observed that notall situations result in more accurate location information, howeverthis is not dispositive of the general benefit of the disclosed subjectmatter. As an example, where a lake is treated as an excluded zone dueto a determined threshold value, a report of a location of a UE that isactually in a boat on the lake can be worse than if the lake was notexcluded via geographic-type SCD, Voronoi-type SCD, historical UEdensity-type SCD, or combinations thereof, e.g., exclusion of the lakecan shift the reported location, such as via a weighted centroid, andcan reduce the reported error value by not including the lake area,where the example UE is actually located, from LQD calculations.However, the use of SCD to improve LQD determination for an identifiedBS will typically improve LQD values where corresponding parameters arereasonably selected, e.g., threshold values for exclusion zones, depthof historical UE density data to prevent homogeneity in an area, shapedetermination parameters for determining a shape based on historical UEdensity data, etc.

To the accomplishment of the foregoing and related ends, the disclosedsubject matter, then, comprises one or more of the features hereinaftermore fully described. The following description and the annexed drawingsset forth in detail certain illustrative aspects of the subject matter.However, these aspects are indicative of but a few of the various waysin which the principles of the subject matter can be employed. Otheraspects, advantages, and novel features of the disclosed subject matterwill become apparent from the following detailed description whenconsidered in conjunction with the provided drawings.

FIG. 1 is an illustration of a system 100, which can facilitatedetermining location quality data based on identification of a basestation device in accordance with aspects of the subject disclosure.System 100 can comprise CellID location quality determination component(LQC) 110. LQC 110 can receive historical cell data 120. Historical celldata 120 can comprise known locations of a base station (BS) and anidentifier for that BS, e.g., a corresponding CellID. This informationcan be based on installation records of BSs, empirical measurements of aradio network, user reported location and CellID information, etc.Historical cell data can be received about a network from data stored byan entity associated with the network. As an example, a wireless networkprovider can, in the course of regular business, store data related tothe identification, location, operations, and various parameters of BSsproviding wireless access to the network, which information can becomprised in historical cell data 120 that can be received by LQC 110.

In an aspect, historical cell data 120 can be employed by LQC 110 todetermine location quality data (LQD) 150. LQD 150 can comprise aquality metric value associated with a location of a UE based on alocation of an identified BS. LQC 110 can receive UE data 130. UE data130 can comprise CellID 132. CellID 132 can identify a BS. As anexample, where a mobile device, e.g., a UE, is within a coverage area ofan access point (AP), the UE can send UE data 130 comprising CellID 132,which identifies the AP, to LQC 110. LQC 110 can then determine based onhistorical cell data 120, a location of the AP identified by CellID 132,e.g., CellID 132 can correspond to a CellID of historical cell data 120to facilitate access to historical data corresponding to the identifiedAP. Historical cell data 120 can comprise the location of the identifiedAP and a type of the identified AP enabling a determination of thecoverage area of the identified AP, e.g., LQC 110 can determine that theidentified AP is a Wi-Fi AP located at the corner of 5^(th) Ave. andMain St. with an omnidirectional coverage area approximated by a 150foot diameter circle. LQD 150 can then be provided indicating that thelocation is associated with a +/−75 foot radius of the location of theAP. In some embodiments, LQD 150 can further provide the location, e.g.,that the identified AP is located at the corner of 5^(th) Ave. and MainSt.+/−75 feet (given in longitude/latitude, or any other convenientlocation format). In an aspect, a 50% error rate can be associated witha uniform distribution of UEs within the service area of an identifiedBS. As such, the root mean square radial measurements from the locationof the BS to the edges of the service coverage area can indicate adistance at which half of the UEs are within the distance to the BS andthe other half of the UEs are between the distance and the edge of theUE. The radial distance between the BS location and the edge of theservice are can then be averaged for each radial measurement taken,mathematically can be performed as a summation, or as an integration ofall radial measurements, e.g., a continuous sweep the RMS distancesbetween the BS location and the extents of the service area. As anexample, where measurements are taken at 0, 90, 180, and 270 degrees,the mean error estimate for the identified BS can be computed as theaverage of (0.707(distance between the BS location and the service edgeat 0 degrees), 0.707(distance between the BS location and the serviceedge at 90 degrees), 0.707(distance between the BS location and theservice edge at 180 degrees), and 0.707(distance between the BS locationand the service edge at 270 degrees)). The mean error estimation canimprove where more RMS distances are taken, e.g., taken every 10 degreescan be better than every 90 degrees, every one degree can be better thanevery 10 degrees, the RMS of the integral of the distanced between theBS location and the service edge from 0 to 360 degrees being better thanevery one degree.

In an embodiment, LQC 110 can improve the error measurement comprised inLQD 150 by employing supplemental cell data (SCD) 140 in determining LQD150. SCD 140 can comprise any combination of Voronoi-type SCD 142,geographic-type SCD 144, historical UE density-type SCD 146, historicalUE timing advance-type SCD 148, etc. SCD 140 can be employed by LQC 110to ignore areas where a UE is less likely to be located, such that thearea where the UE can be located and concurrently in the service area ofan identified BS is reduced, allow a greater degree of confidence in thereported location, e.g., a higher quality of location data that can beassociated with a lower degree of error in comparison to returning alocation of a device with error bounded solely by the claimed servicearea of the identified BS.

Voronoi-type SCD 142 can comprise Voronoi data related to a BS. In anaspect, historical cell data 120 can be associated with Voronoi data,e.g., a wireless radio network can be associated with Voronoi datadefining Voronoi cells for the several BSs of the wireless radionetwork, see Voronoi-type SCD 242, etc. A Voronoi cell can havedifferent extents from extents of a coverage area for the same BS. Thiscan result from BSs generally being located close enough to each otherthat there is often overlap in coverage areas and, whereas the Voronoicell edges relate to distances between BSs that represent pointstypically midway between BSs, the Voronoi cell edges can often representa smaller area than a service area. Voronoi-type SCD 142 therefore canbe an improvement over the extents of a coverage area by reducing thearea that is likely to contain a UE for an identified BS. The average ofthe RMS distance between the BS location and the edges of the Voronoicell can then be used to generate an average mean error that can also bean improvement over the average RMS distance between the BS location andthe extents of the BS coverage area. In some embodiments, Voronoi-typeSCD can be generated by LQC 110 based on historical cell data 120 or, insome embodiments, Voronoi-type SCD 142 can be received by LQC 110comprised in SCD 140, while in some embodiments, Voronoi-type SCD can beboth generated by LQC 110 and received by LQC 110 via SCD 140.

In an aspect, the Voronoi cell can be employed to determine a centroidlocation that can be different from the location reported for the BS.This centroid location can be employed in determining LQD 150 by LQC110. In an embodiment, LQC 110 can determine the centroid of the Voronoicell, and can determine the average RMS distance between the centroidlocation and the edges of the Voronoi cell in a manner similar to thatdisclosed herein for determining the average of the RMS distance fromthe location of the BS to the edges of the Voronoi cell. This can be afurther improvement of LQD 150 in that the centroid can be substitutedfor the UE location, rather than the location of the BS, as itrepresents a center of the Voronoi cell and the average of the RMSdistance can therefore be smaller with the centroid than with the BSlocation.

Geographic-type SCD 144 can comprise geographic data corresponding togeographical aspects of an area associated with an identified BS.Geographic data can comprise topographical map information, roadwayinformation, etc. As an example, geographic data can comprise a locationof a lake, graveyard, airport, steep cliff, timber forest, etc. Anexample lake can be associated with an area that a UE is unlikely to belocated. As such, the area of the lake can be removed from possibleareas a UE can be located within a service area of a BS or a Voronoicell for the BS. This can reduce the error associated with substitutinga location of the BS for the location of the UE thereby increasing thequality of the location data. As an example, where a lake occupies aportion of a service area, and correspondingly a portion of a Voronoicell, for an identified BS, the extents of the lake can be removed fromthe computations of the average RMS distances between the BS locationand the edge of the service area or the edge of the Voronoi cell, e.g.,rather than using an RMS from the BS to the edge of the Voronoi cellover water, LQC 110 can use the RMS value from the BS to the edge of thelake as defined in geographic-type SCD 144. Similarly, the lake can beremoved from the average RMS values determined based on the extents ofthe service area rather than the edges of the Voronoi cell.

In an aspect, the geographic data can be employed to determine ageographic centroid location that can be different from the locationreported for the BS and/or the centroid of a Voronoi cell. Thisgeographic centroid location can be employed in determining LQD 150 byLQC 110. In an embodiment, LQC 110 can determine the geographic centroidof the Voronoi cell sans excluded areas determined form the geographicdata, and can determine the average RMS distance between the geographiccentroid location and the edges of the Voronoi cell as modified by theexclusion areas. This can be a further improvement of LQD 150 in thatthe geographic centroid can be substituted for the UE location, ratherthan the location of the BS, where it represents a center of the Voronoicell as modified by exclusion areas, and the average of the RMS distancecan therefore be smaller with the geographic centroid than with the BSlocation or the centroid of the Voronoi cell unmodified by any exclusionarea(s) derived from the geographic data.

Historical UE density-type SCD 146 can comprise historical UE densitydata. Historical UE density data can relate to or indicate a count ofUEs in an area or ‘bin’. In an aspect the ‘bin’ can be defined by aregular granular division of a larger area into one or more bins, e.g.,100 meter×100 meter bins. A count of UEs for each bin can correlate toUE density for the larger area. In an aspect, historical UE density datacan generate a heat map showing historically dense UE locations. Whereasthe UEs comprising historical UE density data can be correlated with aCellID, the historical UE density data can further indicate which BS sthe UE saw at the reported location/bin. This can allow correlationbetween a BS and an area of service directly from historical UE densitydata. In an aspect, LQC 110 can determine a shape of the coverage areafor a BS based on historical UE density-type SCD 146. In another aspect,LQC 110 can receive a shape of a coverage area for a BS based onhistorical UE density data via historical UE density-type SCD 146.

In an embodiment, historical UE density data can be employed by LQC 110to generate LQD 150. Where a service area can be derived from historicalUE density data, LQC 110 can determine an average RMS of distancesbetween the BS location and the extents of the device area determinedform the historical UE density data. In an aspect, the historical UEdensity data can naturally include features more commonly associatedwith geographic data, e.g., a lower density of UEs can be expected inthe middle of lake than in the middle of a grocery store near the lake.As such, the average RMS distance measurements can be an improvementover simply reporting the extents of the BS that, for example, canignore the lake or other low UE density areas. In an aspect, historicalUE density data can be combined with geographic-type SCD 144 that canhave, for example, more precise measurements of the lake edge than mightbe found in the historical UE density data.

Moreover, where historical UE density data comprises known locations ina service area, LQD 150 can be determined by averaging distances betweenthe BS location and a bin for each reporting UE of the historical UEdensity data. As an example, where a BS has a 2000 meter diameterservice area and where 10 UEs report from 400 meters away from the BSlocation and 1000 UEs report from 800 meters away for the BS location,the error can be ((10*400 meters)+(100*800 meters))/110)=763 meters,which illustrates an improvement over the +/−1000 meter error that wouldhave been reported based only on the coverage area of the BS.

In an embodiment, historical UE density data can be combined withVoronoi-type SCD 142. The combination can employ the Voronoi cell toselect which historical UE density data to employ, in contrast to usingthe historical UE density data to determine which historical UE densitydata is correlated to a BS. The use of the Voronoi cell to selecthistorical UE density data can, again because it can generally be asmaller area than an extent of a service area for the BS, provide animproved error measurement in LQD 150. In some embodiments, thehistorical UE density-type SCD 146 can be combined with bothgeographic-type SCD 144 and Voronoi-type SCD 142, e.g., providingdensity information within a Voronoi cell and excluding a measured edgeof a lake, etc.

In a further aspect, historical UE density data from historical UEdensity-type SCD 146, can be employed to determine a weighted centroid.The weighted centroid can shift the centroid towards higher densitylocations and away from lower density locations in the service area,e.g. a UE is more likely to be located at a historically dense area ascompared to a historically sparse area. The historical UE density data,as disclosed above, can be employed to compute the average distancebetween the weighted centroid location and each of the reporting UElocations comprised in the historical UE density data. This can furtherimprove LQD 150 in that not only can the UE location be reported as theweighted centroid, but also error associated with the average distanceis based on the weighted centroid to reduce the error. In an aspect,this is a statistical manipulation that results in a majority of UEqueries reporting with improved location and higher quality, at theexpense of a minority of UE queries that will return a more incorrectlocation at decreased quality. As an example, if 98 UEs are located at Aand 2 UEs are located at B, then the weighted centroid will be muchcloser to A than to B, correspondingly the distance between the weightedcentroid and the 98 UEs at A will overpower the distance between theweighted centroid and the two UEs at B when averaged, as such, when a UElocation query comes in (for the 101^(st) UE), the location can be givenas the weighted centroid with an error that is biased by the 98 UEslocated at A. Therefore, if the 101^(st) UE is actually located at A,the weighted centroid location and LQD will be much more accurate than,where the 101^(st) UE is actually located at B. This can be interpretedas saying, mathematically speaking, that historically most UEs are at A,and if we have to guess at a location of the 101^(st) UE, we willpresume it is also most likely at A, and if it is actually at B thenstatistically it is an outlier.

Historical UE timing advance-type SCD 148 can comprise historical UEtiming advance (TA) data. Historical UE TA data can relate to timingadvance values associated with historical UE reporting for a determinedBS. Generally speaking, in wireless communications, timing advance datais employed in scheduling communications between a BS and UE to accountfor propagation of a radio signal across a given distance. The farther aradio signal travels the longer it takes to propagate. Timing advance istypically measure in units of ‘chip’ where a chip represents the changein round-tip propagation distance, e.g., twice the propagation range.Each unit of chip represents can correspond to an offset time value, forexample 3.7 microseconds, such that the difference of chip in timingadvance represents a distance gap based on the offset time valuemultiplied by the value of a chip. As an example a four chip timingadvance can be (4*3.7)=14.8 microseconds, while a three chip timingadvance can be (3*3.7)=11.1 microseconds and the timing gap between afour chip and three chip timing advance is 3.7 microseconds. Where aradio signal propagates near the speed of light in typical wirelessnetwork operating environments, the chip value corresponds to around-trip distance the radio signal propagates in said time. As anexample, one chip can correspond to a distance of 3.7 microseconds*300meters/microsecond=about 1100 meters round-trip, e.g., 550 metersbetween the UE and the BS. As such, a four chip timing advance can be atabout 4400 meters (2200 meters between the UE and the BS) and a threechip timing advance can be at about 3300 meters (1650 meters between theUE and the BS). As such, a gap between devices reporting four chip andthe devices reporting three chip TA values is 550 meters wide (1100meters round-trip). This can be represented by a annular region that is550 meters wide, starting at 1650 meters from the BS and ending at 2200meters from the BS, e.g., a UE reporting three chip TA can be anywherein the 550 meter wide annular region starting at 1650 meters from the BSup to 220 meters from the BS (where it would then report as a four chipTA). In a sectorized BS, the region for the UE can be further narrowedto an arc of 550 meters deep across the width of the sector, e.g.,120-degrees, etc., forming a ‘banana’ shape.

Historical cell data 120 can be combined with historical UE timing data,e.g., from historical UE timing advance-type SCD 148, etc., toprecompute areas associated with a BS, or sector of a BS, at a given TAvalue. As such, when UE data 130 comprises a TA value, the TA value canbe employed to look up the precomputed region that will constrain the UElocation. Moreover, in some embodiments, TA rings or bananas can becomputed on the fly, rather than being precomputed, such that UE TA data134, when included in UE data 130, can be employed to determine a regionfor BS identified by CellID 132. Where a constraint region associatedwith TA data is determined, the UE is likely, if not assuredly, in theconstraint region, allowing a more accurate reporting of location, e.g.,a location in the constraint region and a correspondingly lower degreeof error in the reported location. In an aspect, the centroid of theconstraint region associated with the timing advance can be employed asthe location and the error can be the mean distance between the locationand the edges of the constraint region. As an example, where a TA bananais determined to be a constraint region, then the location can bereported as the centroid of the TA banana and the error can be the meandistance between the centroid location and the edges of the constraintregion. In an embodiment, for an omnidirectional BS, the location of theBS can be the same as the centroid of the annular ring constraint regionfrom TA data, the error can be improved to be the mean distance to theouter edge of the annular region. While use of TA data alone can beuseful in improving LQD 150, historical UE TA data can be combined withother SCD 140 to provide further improvement.

In an aspect, historical UE TA data can be combined with Voronoi celldata. Combining the Voronoi cell area with the annular region or bananadetermined from TA data can further reduce possible areas in which a UEcan be located. However, this is typically only useful where the UE isreporting TA that corresponds to TA constraint regions that are near,at, or beyond a Voronoi cell edge for a determined BS, e.g., a TA ringor banana near to the BS and not interacting with a Voronoi cell edgeremains similar to just using historical UE TA-type SCD 148 alone.

In another aspect, historical UE TA data can be combined withgeographic-type SCD 144. Where a geographic feature associated withexcluding UEs interacts with a TA annular region or TA banana, this canfurther reduce an area where the UE can be and correspondingly reducethe error in the reported location. As an example, where a causewaycrosses a large body of water and a TA banana arcs across the causeway,see FIG. 6, etc., it can be reasonable to presume that a UE will be onthe road and within the banana, which region can be substantiallysmaller than the area associated with the entire service area of the BS,the area defined by the entire causeway within the BS service area, orthe area circumscribed by the TA banana. As such, the centroid of theintersection of the causeway and the TA banana can be reported as thelocation of the UE and the error can be the mean distance from thecentroid to the extents of the intersection between the causeway and theTA banana.

In a further aspect, historical UE TA data can be combined withhistorical UE density-type SCD 146. Historically dense areas of UEpopulation in a region of a TA annular area or TA banana can improve LQD150. Where a TA annular region overlaps a shopping mall in one areawhile the rest of the annual region overlaps forest or fields cansuggest that a UE is more likely to be in the shopping mall area than inthe forest or fields. As such, a union of a shape defined by thehistorical UE density can be employed to determine a centroid of thearea of the union, or in some embodiments a weighted centroid of thearea of the union to yield a location of the UE and corresponding errormeasured, for example, as the average distance between the centroid andthe extents of the area of the union or the locations of the reportingUEs in the historical data, from the weighted centroid to the extents ofthe area of the union or the locations of the reporting UEs in thehistorical data, etc.

In some embodiments, the presently disclosed subject matter can enablecombining one or more of Voronoi-type SCD 142, geographic-type SCD 144,historical UE density-type SCD 146, historical UE TA-type SCD 148, etc.,to reduce possible areas in which a UE is likely to be located, suchthat an improved location can be reported and a correspondingly improvedLQD 150 can be reported, e.g., a lower error inherent in the locationprovided. As an example, historical UE density data can indicate a stripof freeway that has high UE densities, which can be combined withgeographic data to better define the edges of the freeway, which can becombined with a Voronoi cell edge to reduce the amount of freewayconsidered as a probable location for a UE, which can be combined with aTA value that corresponds to a TA banana crossing the freeway. Thisexample combination can result in, for example, a 550-meter long stretchof the freeway as a likely location for any UE seeing the correspondingBS. Where the historical density is generally homogeneous on the stretchof freeway in the example, the geographic centroid of the freeway can beused as the location for a UE and the error, e.g., LQD 150, can bedetermined to be the mean distance from the centroid to the extents ofthe freeway as determined from the geographic data, which can be ahigher quality than the conventional technique of asserting the BSlocation as the UE location with an error that is the diameter of the BSservice area.

LQC 110 can further receive UE data 130. UE data 130 can comprise anycombination of CellID 132, UE TA data 134, UEID 136, etc. UE data 130can enable selection of corresponding historical cell data 120 by LQC110, e.g., CellID 132 can enable LQC 110 to employ historical cell data120 corresponding to the identified BS. Similarly, for example, UE TAdata 134 can enable LQC 110 to employ historical TA rings or bananas, orcalculate these on the fly, for the same TA value in historical celldata 120 for the identified BS.

FIG. 2 is an illustration of examples of supplementary cell data 200that can be employed in determining location quality data based onidentification of a base station device in accordance with aspects ofthe subject disclosure. System 200 illustrates, at 242, an examplevisualization of Voronoi-type SCD, wherein each black square representsa BS and the Voronoi cell edges can be seen surrounding the illustratedBSs. Additionally, a geographic region, e.g., a lake, is illustrated. Asdisclosed herein above, the example lake can more typically beassociated with geographic-type SCD, e.g., 244, etc., but is illustratedat 242 to show how it can alter a Voronoi cell edge, e.g., causing theaffected Voronoi cell to be smaller in area. Whereas Voronoi mathematicsis well documented, further discussion of determining Voronoi cells isbeyond the scope of the instant disclosure other than to state that allforms of determining a Voronoi edge can be employed in determining LQD,e.g., 150, etc., as disclosed herein.

System 200 illustrates, at 244, and example visualization ofgeographic-type SCD. As can be seen, the black squares again representBSs, such as those illustrated in 242. Moreover, a geographic element,e.g., a lake is also illustrated, and in an instance can correspond tothe lake in 242. The lake can represent a geographic area where a UE isunlikely to be located. Determining which geographic areas are unlikelyto comprise a UE is generally beyond the scope of the presentdisclosure, although any such determination can be employed indetermining LQD, e.g., 150, as disclosed herein. As an example, allbodies of water can be determined to exclude UEs, bodies of waterexceeding a determined threshold area can be determined to exclude UEs,etc. In an aspect, geographic-type SCD 244 can provide definedcoordinates along edges of geographic features that can be, for example,more accurate than can be determined by other techniques, e.g., UEdensity mapping, etc. As such, combining geographic-type SCD 244 canimprove LQD determination.

System 200 illustrates, at 246, an example visualization of historicalUE density-type SCD. Historical UE density-type data can comprisehistorical counts of UEs in areas comprising a region, which can be seenas shaded circles overlaying a map of a region. In the illustration,darker shading indicates higher UE densities in that area of the map,such that the left side of the pictured area can generally be said tohave a higher UE density than the right side of the pictured area. In anaspect, the visualization of the historical UE density data can beorganized, such as in a grid pattern, as illustrated, although otherorganizations of UE density data are equally as usable in the disclosedsubject matter. Moreover, the example visualization can indicate levelsof UE density in the shading of the circles, e.g., no sharing canindicate less than a determined bottom threshold count of UEs, while thedarkest shading can indicate more than a determined top threshold countof UEs, and intermittent shades can correspond to counts of UEsaccording to tiers of counts between the bottom threshold and the topthreshold counts. In an aspect, actual UE counts can also be employed,despite illustrating levels of density in the example visualization forclarity and brevity. As an example, levels can comprise <5 UEs, 6-100UEs, 101-500 UEs, 501-600 UEs, and >601 UEs. Other examples can includeother determined thresholds for each determined level. In a furtherexample, actual UE counts in each bin can be employed (although this ismore difficult to illustrate, the data can be directly employed withoutconversion to a visualization of the UE density data). The historical UEdensity can be combined, as disclosed herein, with other SCD, e.g., 140,242, 244, 246, etc., to improve determination of LQD, e.g., 150, etc. Asan example, where SCD 246 is combined with SCD 242, the Voronoi edgescan be employed to select the historical UE densities that can beemployed in determining the LQD. As another example, where SCD 246 iscombined with SCD 244, the lake edges can be more precisely defined thanwhere low density areas alone (from SCD 246) are used in determining theLQD, e.g., the bin grid pattern can be more course than a geographicsurvey of the lake edges, which geographic survey data can be providedin SCD 244. In an aspect, although not illustrated, SCD 246 can compriseUE density correlated to one or more BSs.

System 200 illustrates, at 248, an example visualization of historicalUE timing advance-type SCD. The depth of the ring, e.g., from inner edgeto outer edge, can be one chip. In some embodiments, the ring depth canbe other than one chip. Moreover, sectorization of the base station (theillustrated tower) can divide the ring into, for example, three bananashapes. In an aspect, the inner surface of the ring or bananas canrepresent a first timing advance value and the outer surface of the ringor bananas can represent a second timing advance value. In embodimentsof the disclosed subject matter, SCD 248 can be combined with other SCD,e.g., 242, 244, 246, etc., to improve determining LQD, e.g., 150, etc.As an example, SCD 248 can be combined with SCD 246. In this example, aUE can be determined to be located in the left TA banana based on areported TA value and reported BS sector, e.g., via CellID 132, etc.Moreover, the determined TA banana can overlap different historicaldensities of UEs as determined form SCD 246. As such, LQD can bedetermined based on a weighted centroid of the TA banana in view of thehistorical UE densities therein, such that the error is represented byan average distance between the historical reporting UE location and theweighted centroid of the TA banana, which can be an improvement overconventional technologies.

FIG. 3 is an illustration of an example 300 of combining Voronoi-typesupplementary cell data and geographic-type supplementary cell data indetermining location quality data based on identification of a basestation device, in accordance with aspects of the subject disclosure.Example 300 illustrates a Voronoi edge along a radial measurement vectorfrom the indicated base station, e.g., each radial arm extending fromthe base station through the Voronoi edges can be 22.5-degrees apart, asillustrated, etc. The determined RMS value for that arm can be 0.070*thedistance from the base station to the Voronoi edge, as indicated by thedark circles on each radial arm illustrated, exclusive of the armspassing through the lake. The lake, e.g., a geographical area defined asdevoid of UEs, can be determined from geographic-type SCD, e.g., 144,244, etc. The radial arms passing through the lake can therefore have adetermined RMS value that can be 0.070*distance from the base station tothe edge of the lake, e.g., the geographical edge along a radialmeasurement, rather than to the edge of the Voronoi cell passing throughthe lake itself. The use of the lake as a supplemental edge tot ehVoronoi cell can therefore reduce the area in which a UE can be locatedand can result in lower RMS values than would result from the Voronoiedge. As such, the determination of LQD can correspondingly be improved,wherein the average of the RMS values results in a lower value thanwould be determined based strictly on the Voronoi cell edge alone.

FIG. 4 is an illustration of an example 400 of combining Voronoi-typesupplementary cell data, geographic-type supplementary cell data, andhistorical UE density-type supplementary cell data in determininglocation quality data based on identification of a base station devicein accordance with aspects of the subject disclosure. Example 400illustrates an example Voronoi cell associated with the indicated basestation. The Voronoi cell edges can be modified to include a geographicedge. The resulting shape can be employed to determine a geographiccentroid. The geographic centroid can correspond to a smaller area thanwould be represented by either the extents of a coverage area of the BSor the extents of the Voronoi cell alone. The centroid can be at adifferent location than the location of the BS, and the centroidlocation can be substituted for a UE location. An LQD can be determinedbased on the average RMS distance to the edges of the Voronoi cell asmodified by the geographic edge.

Moreover, where historical UE density data is available, e.g., examplevisualization of a four high historical UE density areas and one lowhistorical UE density area, a weighted centroid can be determined. Theweighed centroid can be substituted for the UE location rather than thegeographic centroid or the BS location. Further, the LQD can be based onthe average distance from the weighted centroid to the reporting UEs ofthe historic UE density data, which can represent a further improvementof the error associated with the location data. In an aspect, thehistorical UE density data can be selected based on the Voronoi celledges, the Voronoi cell edges as modified by the geographic edge, ashape determined from the historical UE density data itself, etc. As canbe observed in example 400, the weighted centroid can be located closerto the higher UE density areas than to the lower UE density areas. Insome circumstances, the weighted centroid can be shifted closer todenser UE areas than the geographic centroid or the BS location.Moreover, the use of historical UE density data can result in theaverage distance between the weighted centroid and the reporting UEs tobe less than the average RMS values from either the geographic centroidor the BS to the Voronoi edges of the Voronoi cell, either with orwithout modification by a geographic edge.

FIG. 5 is an illustration an example 500 of employing historical UEdensity-type supplementary cell data, with or without geographic-typesupplementary cell data, in determining location quality data based onidentification of a base station device in accordance with aspects ofthe subject disclosure. Example 500 depicts an area inside the servicearea of a BS. Also illustrated is a geographic edge, e.g., associatedwith an area that is defined as not comprising a threshold count of UEs,such as a lake, cemetery, airport runway, etc. The geographic edge canbe provided via geographic-type SCD, e.g., 144, 244, etc., or can bedetermined based on historical UE densities in an area being below athreshold level. Example 500 illustrates more higher-density UE bins inthe left of the image than in the right of the image, which comprisesmore low historical UE density bins. In an aspect, a geographic centroidcan be determined based on a shape (not illustrated) encompassingdetermined UE density bins associated with an identified BS. LQD canthen be determined by the average RMS value of the distance from thegeographic centroid to the edge of the shape. Moreover, a weightedcentroid can determined based on the shape and the historical UE densitydata. This weighted centroid can be located closer to higher densitybins than to lower density bins. The LQD can then be based on theaverage distance from the weighted centroid to each of the reporting UEscomprised in the historic UE density data for the BS and included in theshape. Whereas the weighted centroid is typically located closer to thehigher density bins, the distance from the weighted centroid to thehigher density bins can affect the average more than the distance to thelower density bins because there are more UEs associated with the higherdensity bins. As the distances for the higher density bins is alsotypically shorter than the distance to the lower density bins, this canreduce the average distance and correspondingly the LQD can be improved.As disclosed above, statistically a UE is more likely to be located atthe higher density bins than the lower density bins, and accordingly,the location for the weighted centroid is shifted toward the higherdensity bins and the error is reduced where the weighted centroid iscloser to more likely location of a UE.

FIG. 6 is an illustration an example 600 of combining historical UEtiming advance-type supplemental cell data and historical UEdensity-type supplementary cell data in determining location qualitydata based on identification of a base station device in accordance withaspects of the subject disclosure. Example 600 illustrates a causewaycrossing a large lake. As such, it can be expected that there can belittle UE presence in the water itself and where the portion of thecauseway is far from land, almost all UEs can be expected to be locatedon the causeway itself. This can be reflected by the two high historicalUE density bins on the causeway. Moreover, the weighted centroid basedof the historical UE density data can be directly between the two highdensity bins. Moreover, a TA banana can be employed to select only thetwo bins that fall within the banana, e.g., from historical UE TA-typeSCD, 148, 248, etc. Further, the extents of the causeway within thebanana can be determined form the historical UE density data, e.g.,there is no UE density in the lake above a threshold level, or can bedetermined form geographic-type SCD, e.g., 144, 244, etc. Wheregeographic-type SCD is employed, the accuracy of the causeway geometrycan be better than can be determined from UE density bins. As such,based on TA data, geographic data, and UE density data, a region can bedetermined in which it can be expected a UE with the same reported TAvalue can be found. This region can be substantially smaller than aregion only associated with a coverage area of a BS, a region onlyassociated with the TA banana, a region only associated with thegeographic data, or a region only based on a shape derived from UEdensity data, etc., can be, particularly where the BS is located farfrom the intersection of the TA banana and the causeway, e.g., where theBS is located at the shore edge and the banana is perhaps severalkilometers from shore. As such, the weighted centroid can be substitutedof the UE location. Moreover, the average distance from the weightedcentroid to the reporting UEs of the historic UE density data can beshort, e.g., covering perhaps one or two lanes of the causeway. As such,example 600 can provide a very low error, e.g., good LQD, for a UE.Where, for example the causeway is perhaps 25 meters wide and the bananais perhaps 500 meters deep, the location of the UE can be at theweighted centroid +/−<250 meters, which, compared to the examplecoverage area of the BS at +/−2 kilometers, is a substantially lowerlocation error leading to the location at the weighted centroid being ofhigh quality.

In view of the example system(s) described above, example method(s) thatcan be implemented in accordance with the disclosed subject matter canbe better appreciated with reference to flowcharts in FIG. 7-FIG. 9. Forpurposes of simplicity of explanation, example methods disclosed hereinare presented and described as a series of acts; however, it is to beunderstood and appreciated that the claimed subject matter is notlimited by the order of acts, as some acts may occur in different ordersand/or concurrently with other acts from that shown and describedherein. For example, one or more example methods disclosed herein couldalternatively be represented as a series of interrelated states orevents, such as in a state diagram. Moreover, interaction diagram(s) mayrepresent methods in accordance with the disclosed subject matter whendisparate entities enact disparate portions of the methods. Furthermore,not all illustrated acts may be required to implement a describedexample method in accordance with the subject specification. Furtheryet, two or more of the disclosed example methods can be implemented incombination with each other, to accomplish one or more aspects hereindescribed. It should be further appreciated that the example methodsdisclosed throughout the subject specification are capable of beingstored on an article of manufacture (e.g., a computer-readable medium)to allow transporting and transferring such methods to computers forexecution, and thus implementation, by a processor or for storage in amemory.

FIG. 7 illustrates a method 700 that facilitates determining locationquality data based on identification of a base station device inaccordance with aspects of the subject disclosure. Method 700, at 710,can comprise receiving supplemental cell data (SCD) in response toreceiving historical cell data. Historical cell data can comprise datarelated to locations of a base station (BS) and a correspondingidentifier for the BS, e.g., a CellID. The location information can bebased on installation records for the BS, empirical measurements in aradio network, user reported locations, CellID information, etc. Networkproviders can typically have much of this historical cell information inexisting databases. Historical cell data can be received about a networkfrom data stored by an entity associated with the network. As anexample, a wireless network provider can, in the course of regularbusiness, store data related to the identification, location,operations, and various parameters of BSs providing wireless access tothe network, which information can be comprised in historical cell data.In an aspect, the historical cell data can represent a network of BSs,wherein the representation comprises the CellIDs, the locations of theBSs, and parameters associated with the BSs, such as, azimuth,elevation, power, coverage areas, etc., typically associated withoperation of the network, e.g., determining coverage holes, coverageextents, service calls, maintenance, repair, etc.

Supplemental cell data (SCD) can comprise any combination ofVoronoi-type SCD, geographic-type SCD, historical UE density-type SCD,historical UE timing advance-type SCD, etc., e.g., 142, 144, 146, 148,242, 244, 246, 248, etc. SCD can be employed to ignore areas where a UEis less likely to be located, such that the area where the UE can belocated and concurrently in the service area of an identified BS isreduced, allowing a greater degree of confidence in the reportedlocation, e.g., a higher quality of location data that can be associatedwith a lower degree of error in comparison to returning a location of adevice with error bounded solely by the claimed service area of theidentified BS.

At 720, method 700 can comprise determining LQD based on the SCDreceived. The SCD and historical cell data can be employed to precomputelocations and associated location error for different combination ofparameters associated with the SCD and the historical cell data. As anexample, where SCD is an empty data set, historical cell data can beused to determine a location that can be given for a UE that can see theBS as the location of the BS itself and, based on the coverage area ofthe BS, the error can be, for example, determined to be the averagediameter of the coverage area, an average RMS value of the extents ofthe coverage area, etc. However, inclusion of SCD can dramaticallyimprove determinations of location error. As an example, Voronoi celldata can be employed to compute an average RMS value between thelocation of the BS and the extents of the Voronoi cell associatedtherewith. Whereas the Voronoi cell can generally have a smaller areathan the extents of the coverage area, the resulting average RMSdistance value can be lower than that for the extents of the coveragearea, resulting in less error associated with the location informationand improved location quality data (LQD). The LQD for the BS can bestored for later use, e.g., via a query targeted to the BS. Similarly,precomputation based on various combinations of the SCD and historicalcell data can be stored for later use. This data can be stored on one ormore storage devices, which devices can be local to a component thatdetermines LQD, remote from a component that determines LQD, ordistributed among different storage devices that can be either local orremote from a component that determines LQD, such as a cloud-basedstorage.

At 730, method 700 can comprise facilitating access to a portion of theLQD by a device. At this point method 700 can end. The access can befacilitated in response to receiving a location quality query andreceiving UE data. UE data can comprise cell identification data(CellID). The CellID can be employed to select LQD corresponding to theidentified BS. As an example, a data store can store LQD data forvarious permutations of SCD for every BS of a wireless network providerin a determined region, e.g., a huge number of data records, and aCellID can be employed to select the LQD for a single identified BS.Moreover, the UE data can comprise UE timing advance (TA) data. The TAdata can similarly be employed to select LQD corresponding to the TAdata, e.g., where the TA data indicates a chip of 25, then LQD for 25chip TA can be selected. Moreover, the TA selection can be restricted tothe BS identified by the CellID, allowing rapid convergence oncorresponding LQD. Moreover, the LQD can be premised on combination ofVoronoi data, geographic data, historical UE density data, etc., suchthe LQD can comprise error metrics that can be substantially improvedover conventional location technologies based on what BSs a UE can see.

FIG. 8 illustrates a method 800 that facilitates determining locationquality data based on identification of a base station device inaccordance with aspects of the subject disclosure. Method 800, at 810,can comprise receiving UE data comprising a CellID. A CellID canidentify a determined BS, e.g., AP, NodeB, eNodeB, femtocell, picocell,etc. The CellID can indicate a specific BS in a wired or wirelessnetwork.

Method 800, at 820, can include receiving SCD based on the UE data. SCDcan comprise one or more of Voronoi-type SCD, geographic-type SCD,historical UE density-type SCD, historical UE timing advance-type SCD,etc., e.g., 142, 144, 146, 148, 242, 244, 246, 248, etc. SCD can beemployed to reduce the effect of low probability for a UE being locatedin an area in regard to determining a location error associated with adetermined location of a UE based on the UE being in the coverage areaof the BS, e.g., the SCD can remove from consideration or reduce aweight afforded to an area where a UE is less likely to be located. Thiscan enable determination of location error that can be improved overerror determined in the absence of SCD, for a UE determined to belocated in a service area of an identified. The lower error can beassociated with a higher quality of location data.

At 830, method 800 can comprise determining LQD based on the SCD. TheLQD can comprise a location error metric. The LQD can be based onvarious permutations combining the SCD with historical cell data, e.g.,a known location of the determined BS. As an example, Voronoi data canbe associated with a smaller area than the extents of a service area fora particular BS. As such, 50% error value can be determined based on anaverage RMS value of distance between the BS location and an extent ofthe Voronoi cell edge, either measured continuously or at angularintervals. Moreover, the inclusion of other SCD can further improve theerror associated with the location, e.g., reduce the associated errorvalue. As an example, geographical data can remove portions of theVoronoi cell from consideration, e.g., areas where a UE is below athreshold value to be located therein, such as a lake, cemetery, largeforested areas, nature preserves, rivers, etc.

At 840, method 800 can comprise enabling access to a portion of the LQDbased on the UE data. At this point method 800 can end. Whereas LQD canbe determined for one or more BSs, the UE data, e.g., CellID, can allowaccess to a portion of the LQD based on an identified BS. In an aspect,a query for a UE location can therefore be based on the UE being in aservice area of a BS, e.g., the UE can see the BS. This can allow dataassociated with typical radio access network access to be employed indetermining a location of the UE, e.g., via substitution of the BSlocation, substitution of a centroid location, substitution of ageographic centroid location, substitution of a weighted centroidlocation, etc., rather than using a more power intensive locationdetermination technology, e.g., GPS, etc. However, conventional locationdetermination via substitution of a BS location can often be associatedwith substantial error, more especially in areas where the BS has alarge coverage area. By employing the disclosed technology, e.g., method800, a location error can be reduced/improved, to allow broader use andacceptance of location determination via substitution of a BS orcorresponding centroid(s). As an example, where a UE can see a BS,method 800 can enable location determination via substitution of aweighted centroid having substantially less location error than can beassociated with substitution of a BS location having an error as largeas the coverage area of the BS. This can allow the example UE to avoidconsuming additional battery power associated with use of a GPS locationtechnology, thereby extending battery life of the UE. Moreover, as theexample UE moves through the wireless network, the location of the UEcan be ascertained based on the UE handshaking with the BSs of thenetwork, allowing queries as to the UE's location without need of anyfurther action on the part of the UE. This can be valuable, for example,in verifying the use of a credit card, e.g., where the point of saleterminal is located in a first location, a second location of a UEassociated with the credit card, such as via a user profile, can bedetermined based on a the UE seeing a nearby BS and, with sufficientlylow error, the second can be compared to the first location to validatethe use of the credit card. Where the error is large, e.g., low LQD, thesecond location can be insufficient to validate the transaction. Assuch, method 800, etc., can enable use of a low power, backgroundenabled, location technology by reducing the location errortraditionally associated with CellID location techniques.

FIG. 9 illustrates a method 900 enabling a UE to receive locationquality data, wherein the location quality data is based onidentification of a base station device, in accordance with aspects ofthe subject disclosure. Method 900, at 910, can comprise generating, byUE, a query comprising UE data and a request for LQD. In an aspect, theUE data can comprise a CellID. UE data, in some embodiments, can furthercomprise UEID, UE TA data, etc. The CellID can be employed to select aportion of LQD determined by another device.

At 920, method 900 can comprise, receiving, by the UE, LQD in responseto the query. At this point method 900 can end. In an aspect, the LQDreceived at 920 can be a portion of LQD determined by another device,stored on a data storage device/network, etc. In an aspect, the LQD canbe based on SCD. In some embodiments, SCD can be combined withhistorical cell data. SCD can comprise Voronoi-type SCD, geographic-typeSCD, historical UE density-type SCD, historical UE timing advance-typeSCD, etc., e.g., 142, 144, 146, 148, 242, 244, 246, 248, etc. Variouscombinations of SCD data can provide different location information andlocation error information, as disclosed herein. In some embodiments,method 900 can be performed for another device rather than a UE. Inthese embodiments, the other device can provide a UEID and CellID as UEdata, so that the location an LQD can be associated with a particular UEfor an indicated BS.

FIG. 10 is a schematic block diagram of a computing environment 1000with which the disclosed subject matter can interact. The system 1000comprises one or more remote component(s) 1010. The remote component(s)1010 can be hardware and/or software (e.g., threads, processes,computing devices). In some embodiments, remote component(s) 1010 cancomprise servers, personal servers, wireless telecommunication networkdevices, RAN device(s), etc. As an example, remote component(s) 1010 canbe a source of UEID 136, UE TA data 134, CellID 132, SCD 140,Voronoi-type SCD, geographic-type SCD, historical UE density-type SCD,historical UE timing advance-type SCD, etc., e.g., 142, 144, 146, 148,242, 244, 246, 248, etc., historical cell data 120, etc., a data storagedevice, a cloud-based data store or virtualized LQC 110, etc.

The system 1000 also comprises one or more local component(s) 1020. Thelocal component(s) 1020 can be hardware and/or software (e.g., threads,processes, computing devices). In some embodiments, local component(s)1020 can comprise, for example, LQC 110, etc.

One possible communication between a remote component(s) 1010 and alocal component(s) 1020 can be in the form of a data packet adapted tobe transmitted between two or more computer processes. Another possiblecommunication between a remote component(s) 1010 and a localcomponent(s) 1020 can be in the form of circuit-switched data adapted tobe transmitted between two or more computer processes in radio timeslots. The system 1000 comprises a communication framework 1040 that canbe employed to facilitate communications between the remote component(s)1010 and the local component(s) 1020, and can comprise an air interface,e.g., Uu interface of a UMTS network, via a long-term evolution (LTE)network, etc. Remote component(s) 1010 can be operably connected to oneor more remote data store(s) 1050, such as a hard drive, solid statedrive, SIM card, device memory, etc., that can be employed to storeinformation on the remote component(s) 1010 side of communicationframework 1040. Similarly, local component(s) 1020 can be operablyconnected to one or more local data store(s) 1030, that can be employedto store information on the local component(s) 1020 side ofcommunication framework 1040. As examples, UEID 136, UE TA data 134,CellID 132, SCD 140, Voronoi-type SCD, geographic-type SCD, historicalUE density-type SCD, historical UE timing advance-type SCD, etc., e.g.,142, 144, 146, 148, 242, 244, 246, 248, etc., historical cell data 120,etc., can be stored on a local data store(s) 1030 of a local component1020.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 11, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that performs particulartasks and/or implement particular abstract data types.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It is noted that thememory components described herein can be either volatile memory ornonvolatile memory, or can comprise both volatile and nonvolatilememory, by way of illustration, and not limitation, volatile memory 1120(see below), non-volatile memory 1122 (see below), disk storage 1124(see below), and memory storage 1146 (see below). Further, nonvolatilememory can be included in read only memory, programmable read onlymemory, electrically programmable read only memory, electricallyerasable read only memory, or flash memory. Volatile memory can compriserandom access memory, which acts as external cache memory. By way ofillustration and not limitation, random access memory is available inmany forms such as synchronous random access memory, dynamic randomaccess memory, synchronous dynamic random access memory, double datarate synchronous dynamic random access memory, enhanced synchronousdynamic random access memory, Synchlink dynamic random access memory,and direct Rambus random access memory. Additionally, the disclosedmemory components of systems or methods herein are intended to comprise,without being limited to comprising, these and any other suitable typesof memory.

Moreover, it is noted that the disclosed subject matter can be practicedwith other computer system configurations, comprising single-processoror multiprocessor computer systems, mini-computing devices, mainframecomputers, as well as personal computers, hand-held computing devices(e.g., personal digital assistant, phone, watch, tablet computers,netbook computers, . . . ), microprocessor-based or programmableconsumer or industrial electronics, and the like. The illustratedaspects can also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network; however, some if not all aspects ofthe subject disclosure can be practiced on stand-alone computers. In adistributed computing environment, program modules can be located inboth local and remote memory storage devices.

FIG. 11 illustrates a block diagram of a computing system 1100 operableto execute the disclosed systems and methods in accordance with anembodiment. Computer 1112, which can be, for example, LQC 110, etc., asource of UEID 136, UE TA data 134, CellID 132, SCD 140, Voronoi-typeSCD, geographic-type SCD, historical UE density-type SCD, historical UEtiming advance-type SCD, etc., e.g., 142, 144, 146, 148, 242, 244, 246,248, etc., historical cell data 120, etc., can comprise a processingunit 1114, a system memory 1116, and a system bus 1118. System bus 1118couples system components comprising, but not limited to, system memory1116 to processing unit 1114. Processing unit 1114 can be any of variousavailable processors. Dual microprocessors and other multiprocessorarchitectures also can be employed as processing unit 1114.

System bus 1118 can be any of several types of bus structure(s)comprising a memory bus or a memory controller, a peripheral bus or anexternal bus, and/or a local bus using any variety of available busarchitectures comprising, but not limited to, industrial standardarchitecture, micro-channel architecture, extended industrial standardarchitecture, intelligent drive electronics, video electronics standardsassociation local bus, peripheral component interconnect, card bus,universal serial bus, advanced graphics port, personal computer memorycard international association bus, Firewire (Institute of Electricaland Electronics Engineers 1194), and small computer systems interface.

System memory 1116 can comprise volatile memory 1120 and nonvolatilememory 1122. A basic input/output system, containing routines totransfer information between elements within computer 1112, such asduring start-up, can be stored in nonvolatile memory 1122. By way ofillustration, and not limitation, nonvolatile memory 1122 can compriseread only memory, programmable read only memory, electricallyprogrammable read only memory, electrically erasable read only memory,or flash memory. Volatile memory 1120 comprises read only memory, whichacts as external cache memory. By way of illustration and notlimitation, read only memory is available in many forms such assynchronous random access memory, dynamic read only memory, synchronousdynamic read only memory, double data rate synchronous dynamic read onlymemory, enhanced synchronous dynamic read only memory, Synchlink dynamicread only memory, Rambus direct read only memory, direct Rambus dynamicread only memory, and Rambus dynamic read only memory.

Computer 1112 can also comprise removable/non-removable,volatile/non-volatile computer storage media. FIG. 11 illustrates, forexample, disk storage 1124. Disk storage 1124 comprises, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, flash memory card, or memory stick. In addition, disk storage1124 can comprise storage media separately or in combination with otherstorage media comprising, but not limited to, an optical disk drive suchas a compact disk read only memory device, compact disk recordabledrive, compact disk rewritable drive or a digital versatile disk readonly memory. To facilitate connection of the disk storage devices 1124to system bus 1118, a removable or non-removable interface is typicallyused, such as interface 1126.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media or communications media, whichtwo terms are used herein differently from one another as follows.

Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media cancomprise, but are not limited to, read only memory, programmable readonly memory, electrically programmable read only memory, electricallyerasable read only memory, flash memory or other memory technology,compact disk read only memory, digital versatile disk or other opticaldisk storage, magnetic cassettes, magnetic tape, magnetic disk storageor other magnetic storage devices, or other tangible media which can beused to store desired information. In this regard, the term “tangible”herein as may be applied to storage, memory or computer-readable media,is to be understood to exclude only propagating intangible signals perse as a modifier and does not relinquish coverage of all standardstorage, memory or computer-readable media that are not only propagatingintangible signals per se. In an aspect, tangible media can comprisenon-transitory media wherein the term “non-transitory” herein as may beapplied to storage, memory or computer-readable media, is to beunderstood to exclude only propagating transitory signals per se as amodifier and does not relinquish coverage of all standard storage,memory or computer-readable media that are not only propagatingtransitory signals per se. Computer-readable storage media can beaccessed by one or more local or remote computing devices, e.g., viaaccess requests, queries or other data retrieval protocols, for avariety of operations with respect to the information stored by themedium. As such, for example, a computer-readable medium can compriseexecutable instructions stored thereon that, in response to execution,cause a system comprising a processor to perform operations, comprisingdetermining LQD based on combinations of UEID 136, UE TA data 134,CellID 132, SCD 140, Voronoi-type SCD, geographic-type SCD, historicalUE density-type SCD, historical UE timing advance-type SCD, etc., e.g.,142, 144, 146, 148, 242, 244, 246, 248, etc., historical cell data 120,etc.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

It can be noted that FIG. 11 describes software that acts as anintermediary between users and computer resources described in suitableoperating environment 1100. Such software comprises an operating system1128. Operating system 1128, which can be stored on disk storage 1124,acts to control and allocate resources of computer system 1112. Systemapplications 1130 take advantage of the management of resources byoperating system 1128 through program modules 1132 and program data 1134stored either in system memory 1116 or on disk storage 1124. It is to benoted that the disclosed subject matter can be implemented with variousoperating systems or combinations of operating systems.

A user can enter commands or information into computer 1112 throughinput device(s) 1136. In some embodiments, a user interface can allowentry of user preference information, etc., and can be embodied in atouch sensitive display panel, a mouse/pointer input to a graphical userinterface (GUI), a command line controlled interface, etc., allowing auser to interact with computer 1112. Input devices 1136 comprise, butare not limited to, a pointing device such as a mouse, trackball,stylus, touch pad, keyboard, microphone, joystick, game pad, satellitedish, scanner, TV tuner card, digital camera, digital video camera, webcamera, cell phone, smartphone, tablet computer, etc. These and otherinput devices connect to processing unit 1114 through system bus 1118 byway of interface port(s) 1138. Interface port(s) 1138 comprise, forexample, a serial port, a parallel port, a game port, a universal serialbus, an infrared port, a Bluetooth port, an IP port, or a logical portassociated with a wireless service, etc. Output device(s) 1140 use someof the same type of ports as input device(s) 1136.

Thus, for example, a universal serial busport can be used to provideinput to computer 1112 and to output information from computer 1112 toan output device 1140. Output adapter 1142 is provided to illustratethat there are some output devices 1140 like monitors, speakers, andprinters, among other output devices 1140, which use special adapters.Output adapters 1142 comprise, by way of illustration and notlimitation, video and sound cards that provide means of connectionbetween output device 1140 and system bus 1118. It should be noted thatother devices and/or systems of devices provide both input and outputcapabilities such as remote computer(s) 1144.

Computer 1112 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1144. Remote computer(s) 1144 can be a personal computer, a server, arouter, a network PC, cloud storage, a cloud service, code executing ina cloud-computing environment, a workstation, a microprocessor basedappliance, a peer device, or other common network node and the like, andtypically comprises many or all of the elements described relative tocomputer 1112. A cloud computing environment, the cloud, or othersimilar terms can refer to computing that can share processing resourcesand data to one or more computer and/or other device(s) on an as neededbasis to enable access to a shared pool of configurable computingresources that can be provisioned and released readily. Cloud computingand storage solutions can storing and/or processing data in third-partydata centers which can leverage an economy of scale and can viewaccessing computing resources via a cloud service in a manner similar toa subscribing to an electric utility to access electrical energy, atelephone utility to access telephonic services, etc.

For purposes of brevity, only a memory storage device 1146 isillustrated with remote computer(s) 1144. Remote computer(s) 1144 islogically connected to computer 1112 through a network interface 1148and then physically connected by way of communication connection 1150.Network interface 1148 encompasses wire and/or wireless communicationnetworks such as local area networks and wide area networks. Local areanetwork technologies comprise fiber distributed data interface, copperdistributed data interface, Ethernet, Token Ring and the like. Wide areanetwork technologies comprise, but are not limited to, point-to-pointlinks, circuit-switching networks like integrated services digitalnetworks and variations thereon, packet switching networks, and digitalsubscriber lines. As noted below, wireless technologies may be used inaddition to or in place of the foregoing.

Communication connection(s) 1150 refer(s) to hardware/software employedto connect network interface 1148 to bus 1118. While communicationconnection 1150 is shown for illustrative clarity inside computer 1112,it can also be external to computer 1112. The hardware/software forconnection to network interface 1148 can comprise, for example, internaland external technologies such as modems, comprising regular telephonegrade modems, cable modems and digital subscriber line modems,integrated services digital network adapters, and Ethernet cards.

The above description of illustrated embodiments of the subjectdisclosure, comprising what is described in the Abstract, is notintended to be exhaustive or to limit the disclosed embodiments to theprecise forms disclosed. While specific embodiments and examples aredescribed herein for illustrative purposes, various modifications arepossible that are considered within the scope of such embodiments andexamples, as those skilled in the relevant art can recognize.

In this regard, while the disclosed subject matter has been described inconnection with various embodiments and corresponding Figures, whereapplicable, it is to be understood that other similar embodiments can beused or modifications and additions can be made to the describedembodiments for performing the same, similar, alternative, or substitutefunction of the disclosed subject matter without deviating therefrom.Therefore, the disclosed subject matter should not be limited to anysingle embodiment described herein, but rather should be construed inbreadth and scope in accordance with the appended claims below.

As it employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to comprising, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit, a digital signalprocessor, a field programmable gate array, a programmable logiccontroller, a complex programmable logic device, a discrete gate ortransistor logic, discrete hardware components, or any combinationthereof designed to perform the functions described herein. Processorscan exploit nano-scale architectures such as, but not limited to,molecular and quantum-dot based transistors, switches and gates, inorder to optimize space usage or enhance performance of user equipment.A processor may also be implemented as a combination of computingprocessing units.

As used in this application, the terms “component,” “system,”“platform,” “layer,” “selector,” “interface,” and the like are intendedto refer to a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration and not limitation, both anapplication running on a server and the server can be a component. Oneor more components may reside within a process and/or thread ofexecution and a component may be localized on one computer and/ordistributed between two or more computers. In addition, these componentscan execute from various computer readable media having various datastructures stored thereon. The components may communicate via localand/or remote processes such as in accordance with a signal having oneor more data packets (e.g., data from one component interacting withanother component in a local system, distributed system, and/or across anetwork such as the Internet with other systems via the signal). Asanother example, a component can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry, which is operated by a software or firmwareapplication executed by a processor, wherein the processor can beinternal or external to the apparatus and executes at least a part ofthe software or firmware application. As yet another example, acomponent can be an apparatus that provides specific functionalitythrough electronic components without mechanical parts, the electroniccomponents can comprise a processor therein to execute software orfirmware that confers at least in part the functionality of theelectronic components.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form.

Further, the term “include” is intended to be employed as an open orinclusive term, rather than a closed or exclusive term. The term“include” can be substituted with the term “comprising” and is to betreated with similar scope, unless otherwise explicitly used otherwise.As an example, “a basket of fruit including an apple” is to be treatedwith the same breadth of scope as, “a basket of fruit comprising anapple.”

Moreover, terms like “user equipment (UE),” “mobile station,” “mobile,”subscriber station,” “subscriber equipment,” “access terminal,”“terminal,” “handset,” and similar terminology, refer to a wirelessdevice utilized by a subscriber or user of a wireless communicationservice to receive or convey data, control, voice, video, sound, gaming,or substantially any data-stream or signaling-stream. The foregoingterms are utilized interchangeably in the subject specification andrelated drawings. Likewise, the terms “access point,” “base station,”“Node B,” “evolved Node B,” “eNodeB,” “home Node B,” “home accesspoint,” and the like, are utilized interchangeably in the subjectapplication, and refer to a wireless network component or appliance thatserves and receives data, control, voice, video, sound, gaming, orsubstantially any data-stream or signaling-stream to and from a set ofsubscriber stations or provider enabled devices. Data and signalingstreams can comprise packetized or frame-based flows. Data or signalinformation exchange can comprise technology, such as, multiple-inputand multiple-output (MIMO) radio(s), long-term evolution (LTE), LTEtime-division duplexing (TDD), global system for mobile communications(GSM), GSM EDGE Radio Access Network (GERAN), Wi Fi, WLAN, WiMax,CDMA2000, LTE new radio-access technology (LTE-NX), massive MIMOsystems, etc.

Additionally, the terms “core-network”, “core”, “core carrier network”,“carrier-side”, or similar terms can refer to components of atelecommunications network that typically provides some or all ofaggregation, authentication, call control and switching, charging,service invocation, or gateways. Aggregation can refer to the highestlevel of aggregation in a service provider network wherein the nextlevel in the hierarchy under the core nodes is the distribution networksand then the edge networks. UEs do not normally connect directly to thecore networks of a large service provider but can be routed to the coreby way of a switch or radio access network. Authentication can refer todeterminations regarding whether the user requesting a service from thetelecom network is authorized to do so within this network or not. Callcontrol and switching can refer determinations related to the futurecourse of a call stream across carrier equipment based on the callsignal processing. Charging can be related to the collation andprocessing of charging data generated by various network nodes. Twocommon types of charging mechanisms found in present day networks can beprepaid charging and postpaid charging. Service invocation can occurbased on some explicit action (e.g. call transfer) or implicitly (e.g.,call waiting). It is to be noted that service “execution” may or may notbe a core network functionality as third party network/nodes may takepart in actual service execution. A gateway can be present in the corenetwork to access other networks. Gateway functionality can be dependenton the type of the interface with another network.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer,”“prosumer,” “agent,” and the like are employed interchangeablythroughout the subject specification, unless context warrants particulardistinction(s) among the terms. It should be appreciated that such termscan refer to human entities or automated components (e.g., supportedthrough artificial intelligence, as through a capacity to makeinferences based on complex mathematical formalisms), that can providesimulated vision, sound recognition and so forth.

Aspects, features, or advantages of the subject matter can be exploitedin substantially any, or any, wired, broadcast, wirelesstelecommunication, radio technology or network, or combinations thereof.Non-limiting examples of such technologies or networks comprisebroadcast technologies (e.g., sub-Hertz, extremely low frequency, verylow frequency, low frequency, medium frequency, high frequency, veryhigh frequency, ultra-high frequency, super-high frequency, terahertzbroadcasts, etc.); Ethernet; X.25; powerline-type networking, e.g.,Powerline audio video Ethernet, etc.; femtocell technology; Wi-Fi;worldwide interoperability for microwave access; enhanced general packetradio service; third generation partnership project, long termevolution; third generation partnership project universal mobiletelecommunications system; third generation partnership project 2, ultramobile broadband; high speed packet access; high speed downlink packetaccess; high speed uplink packet access; enhanced data rates for globalsystem for mobile communication evolution radio access network;universal mobile telecommunications system terrestrial radio accessnetwork; or long term evolution advanced.

The term “infer” or “inference” can generally refer to the process ofreasoning about, or inferring states of, the system, environment, user,and/or intent from a set of observations as captured via events and/ordata. Captured data and events can include user data, device data,environment data, data from sensors, sensor data, application data,implicit data, explicit data, etc. Inference, for example, can beemployed to identify a specific context or action, or can generate aprobability distribution over states of interest based on aconsideration of data and events. Inference can also refer to techniquesemployed for composing higher-level events from a set of events and/ordata. Such inference results in the construction of new events oractions from a set of observed events and/or stored event data, whetherthe events, in some instances, can be correlated in close temporalproximity, and whether the events and data come from one or severalevent and data sources. Various classification schemes and/or systems(e.g., support vector machines, neural networks, expert systems,Bayesian belief networks, fuzzy logic, and data fusion engines) can beemployed in connection with performing automatic and/or inferred actionin connection with the disclosed subject matter.

What has been described above includes examples of systems and methodsillustrative of the disclosed subject matter. It is, of course, notpossible to describe every combination of components or methods herein.One of ordinary skill in the art may recognize that many furthercombinations and permutations of the claimed subject matter arepossible. Furthermore, to the extent that the terms “includes,” “has,”“possesses,” and the like are used in the detailed description, claims,appendices and drawings such terms are intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transitional word in a claim.

What is claimed is:
 1. A system, comprising: a processor; and a memorythat stores executable instructions that, when executed by theprocessor, facilitate performance of operations, comprising: determiningan edge location based on a shape defined by a combination of Voronoidata and exclusion area data, wherein the Voronoi data represents aportion of a Voronoi cell related to a base station device, and whereinthe exclusion area data represents a portion of the Voronoi celldetermined not to comprise a first user equipment; and in response to alocation inquiry, providing a location error value based on a basestation location of the base station device and the edge location. 2.The system of claim 1, wherein the exclusion area data is determined notto comprise the first user equipment based on a geographic featureassociated with the Voronoi cell.
 3. The system of claim 2, wherein thegeographic feature is a body of water.
 4. The system of claim 2, whereinthe geographic feature is a terrestrial feature.
 5. The system of claim2, wherein the geographic feature is a designated land use feature. 6.The system of claim 2, wherein the geographic feature is determined fromhistorical user equipment density data.
 7. The system of claim 1,wherein a user equipment location of a second user equipment is based onan adjusted base station location that has been modified based on thebase station location and the exclusion area data.
 8. The system ofclaim 1, wherein the edge location is a first edge location and whereinthe determining the edge location results in the first edge locationbeing closer to the base station location than a second edge locationbased on the Voronoi edge rather than the combination of the Voronoidata and the exclusion area data.
 9. The system of claim 1, wherein thedetermining the edge location comprises determining a root mean squareof an average distance between the base station location and points onthe shape defined by the combination of the Voronoi data and theexclusion area data.
 10. The system of claim 9, wherein the points onthe shape defined by the combination of the Voronoi data and theexclusion area data are incrementally spaced along the edge of theshape.
 11. The system of claim 9, wherein the points on the shapedefined by the combination of the Voronoi data and the exclusion areadata are continuously spaced along the edge of the shape.
 12. A method,comprising: determining, by a system comprising a processor, an edgelocation based on Voronoi data and exclusion area data, wherein theVoronoi data represents a region of a Voronoi cell for a base stationdevice, and wherein the exclusion area data represents a region of theVoronoi cell that does not provide wireless service to at least athreshold number of user equipments; and in response to a request foruser equipment location data, providing, by the system, access to alocation error value determined from at least a base station location ofthe base station device and the edge location.
 13. The method of claim12, wherein the exclusion area data is determined not to providewireless service to at least the threshold number of user equipmentsbased on identification of a geographic feature associated with theVoronoi cell.
 14. The method of claim 12, wherein the exclusion areadata is determined not to provide wireless service to at least thethreshold number of user equipments based on historical user equipmentdensity data corresponding to the Voronoi cell.
 15. The method of claim12, wherein the providing the access to the user equipment location datacomprises determining the location error value, and wherein the edgelocation is a first edge location that is closer to the base stationlocation than a second edge location based on the Voronoi edge ratherthan a combination of the Voronoi data and the exclusion area data. 16.A non-transitory machine-readable medium, comprising executableinstructions that, when executed by a processor, facilitate performanceof operations, comprising: determining an edge location based on Voronoidata and exclusion area data, wherein the Voronoi data represents aregion of a Voronoi cell for a base station device, and wherein theexclusion area data represents a region of the Voronoi cell determinedto satisfy a rule relating to supporting wireless communication forfewer than a defined number of user equipments; and storing a locationerror value corresponding to the base station device at a data store,wherein the location error value is based on a base station location ofthe base station device and the edge location.
 17. The non-transitorymachine-readable medium of claim 16, wherein the exclusion area data isdetermined not to comprise a first user equipment based on a portion ofa geographic feature associated with the Voronoi cell.
 18. Thenon-transitory machine-readable medium of claim 16, wherein thedetermining the edge location comprises determining a root mean squareof an average distance between the base station location and points onthe shape defined by a combination of the Voronoi data and the exclusionarea data.
 19. The non-transitory machine-readable medium of claim 18,wherein the points on the shape defined by the combination of theVoronoi data and the exclusion area data are incrementally spaced alongthe edge of the shape.
 20. The non-transitory machine-readable medium ofclaim 18, wherein the points on the shape defined by the combination ofthe Voronoi data and the exclusion area data are continuously spacedalong the edge of the shape.